Fair hiring
Fair by design, and you can prove it
Good intentions aren’t enough, and more and more laws agree. Talunly builds fairness into every step, and gives you the numbers to check it.
Structured interviews
Every interviewer rates the same criteria from 1 to 4, with an overall recommendation. Nobody sees anyone else’s feedback until they’ve submitted their own, so the first or loudest opinion doesn’t decide.
Private self-identification
An optional EEO section on every application. Answers are stored separately. The hiring team and the AI can never see them, and they only appear as anonymised totals.
Adverse impact report
Selection rates by gender, race/ethnicity and both combined: who moved forward, got an interview, was hired, or scored highly with AI. Impact ratios under the four-fifths rule are flagged, and groups under 5 people are hidden.
An export for your auditor
A CSV of the same numbers for any period and job, for the independent bias audit that laws like NYC Local Law 144 require. Every export is logged.
AI opt-out where required
New York City roles automatically offer a review without AI, and you can offer it anywhere. Those applications get a neutral “Manual review” label.
A record of every decision
Every move, rejection and hire records who made it, the reason, and what AI information they could see. The log can’t be edited.
Reasons, not guesses
Rejections need a reason, and reports show the reasons across your pipeline, so patterns are easy to spot.
Knockouts flag, never reject
Screening questions can flag an answer for a closer look. They never reject anyone automatically.
These tools support compliance; they don’t replace legal advice or an independent audit where the law requires one.